{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc4 as pm\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "@pm.model\n",
    "def mixture(n_groups, n_points):    \n",
    "    centers = pm.Normal(mu=tf.zeros((n_groups, 2)), sigma=1, name='centers')\n",
    "    scales = pm.HalfNormal(0.4 * tf.ones(n_groups), name='scales')\n",
    "    rates = pm.Dirichlet(a=tf.ones(n_groups), name='rates')\n",
    "    \n",
    "    group_assignments = pm.Multinomial(n=n_points, p=rates, name='group_assignments')\n",
    "    \n",
    "    for idx in range(n_groups):\n",
    "        count = group_assignments[idx]\n",
    "        pm.Normal(mu=centers[idx] * tf.ones((count, 2)), sigma=scales[idx], name=f'group_{idx}')\n",
    "    \n",
    "    \n",
    "model = mixture.configure(n_groups=5, n_points=10_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0.06926113 0.44744077 0.01934566 0.12619576 0.3377567 ], shape=(5,), dtype=float32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forward_sample = model.forward_sample()\n",
    "\n",
    "centers = forward_sample['centers'].numpy()\n",
    "scales = forward_sample['scales'].numpy()\n",
    "group_assignments = forward_sample['group_assignments'].numpy()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "\n",
    "for idx, (center, scale, count) in enumerate(zip(centers, scales, group_assignments)):\n",
    "    group = forward_sample[f'group_{idx}']\n",
    "    center_str = ', '.join(f'{c:.2f}' for c in center)\n",
    "    label = f'{idx}: {int(count):,d} points ~ Normal(({center_str}), {scale:.3f})'\n",
    "    ax.plot(group[:, 0], group[:, 1], '.', alpha=0.5, label=label)\n",
    "    ax.plot(center[0], center[1], 'ko')\n",
    "    ax.text(center[0], center[1], str(idx), size=15)\n",
    "\n",
    "ax.legend();\n",
    "print(forward_sample['rates'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different ways to evaluate logp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<pymc4.random_variables.continuous.Normal at 0xb3459a128>,\n",
       " <pymc4.random_variables.continuous.HalfNormal at 0xb348944e0>,\n",
       " <pymc4.random_variables.continuous.Normal at 0xb34894710>,\n",
       " <pymc4.random_variables.continuous.Normal at 0xb34894898>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@pm.model(auto_name=True)\n",
    "def t_test(sd_prior='half_normal'):\n",
    "    mu = pm.Normal(0, 1)\n",
    "    sd = pm.HalfNormal(1)\n",
    "    y_0 = pm.Normal(0, 2 * sd)\n",
    "    y_1 = pm.Normal(mu, 2 * sd)\n",
    "\n",
    "model = t_test.configure()\n",
    "\n",
    "model._forward_context.vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "func = model.make_log_prob_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logp_wrapper_xla():\n",
    "    logp, grad = xla.compile(logp_wrapper, inputs=[])\n",
    "    return logp, grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "logp, write = logp_wrapper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "209 µs ± 15.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-54.939014, None]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "%timeit sess.run([logp, write])\n",
    "sess.run([logp, write])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.72 ms ± 160 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=191812, shape=(), dtype=float32, numpy=-inf>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func = model.make_log_prob_function()\n",
    "\n",
    "mu = tf.ones((10,))\n",
    "sd = tf.ones((10,))\n",
    "y_0 = tf.ones((10,))\n",
    "y_1 = tf.ones((10,))\n",
    "%timeit logp = func(mu, sd, y_0, y_1)\n",
    "func(mu, sd, y_0, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "logp_func_defun = tf.function(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "339 µs ± 60.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=192038, shape=(), dtype=float32, numpy=-inf>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = tf.ones((10,))\n",
    "sd = tf.ones((10,))\n",
    "y_0 = tf.ones((10,))\n",
    "y_1 = tf.ones((10,))\n",
    "%timeit logp = logp_func_defun(mu, sd, y_0, y_1)\n",
    "logp_func_defun(mu, sd, y_0, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=192409, shape=(10,), dtype=float32, numpy=array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], dtype=float32)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu = tf.ones((10,))\n",
    "sd = tf.ones((10,))\n",
    "y_0 = tf.ones((10,))\n",
    "y_1 = tf.ones((10,))\n",
    "\n",
    "with tf.GradientTape() as tape:\n",
    "    tape.watch(mu)\n",
    "    tape.watch(sd)\n",
    "    tape.watch(y_0)\n",
    "    tape.watch(y_1)\n",
    "\n",
    "    logp = logp_func_defun(mu, sd, y_0, y_1)\n",
    "\n",
    "tape.gradient(logp, mu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "758 µs ± 28.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "with tf.GradientTape() as tape:\n",
    "    tape.watch(mu)\n",
    "    tape.watch(sd)\n",
    "    tape.watch(y_0)\n",
    "    tape.watch(y_1)\n",
    "    logp = logp_func_defun(mu, sd, y_0, y_1)\n",
    "\n",
    "tape.gradient(logp, [mu, sd, y_0, y_1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "453 µs ± 72.1 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: id=501179, shape=(), dtype=float32, numpy=-inf>,\n",
       " [<tf.Tensor: id=501180, shape=(10,), dtype=float32, numpy=array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], dtype=float32)>,\n",
       "  <tf.Tensor: id=501181, shape=(10,), dtype=float32, numpy=array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], dtype=float32)>,\n",
       "  <tf.Tensor: id=501182, shape=(10,), dtype=float32, numpy=\n",
       "  array([-0.03383382, -0.03383382, -0.03383382, -0.03383382, -0.03383382,\n",
       "         -0.03383382, -0.03383382, -0.03383382, -0.03383382, -0.03383382],\n",
       "        dtype=float32)>,\n",
       "  <tf.Tensor: id=501183, shape=(10,), dtype=float32, numpy=array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.], dtype=float32)>])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def logp_and_grad(*args):\n",
    "    logp = func(*args)\n",
    "    return logp, tf.gradients(logp, args)\n",
    "\n",
    "logp_grad_func_defun = tf.function(logp_and_grad)\n",
    "\n",
    "mu = tf.ones((10,))\n",
    "sd = tf.ones((10,))\n",
    "y_0 = tf.ones((10,))\n",
    "y_1 = tf.ones((10,))\n",
    "%timeit logp = logp_grad_func_defun(mu, sd, y_0, y_1)\n",
    "logp_grad_func_defun(mu, sd, y_0, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.compiler import xla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow.compiler.xla' has no attribute 'compile'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-f656bb509f47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlogp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mlogp_wrapper_xla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0minitializer_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 423\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializer_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    424\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m    365\u001b[0m     self._concrete_stateful_fn = (\n\u001b[1;32m    366\u001b[0m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0;32m--> 367\u001b[0;31m             *args, **kwds))\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1322\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_signature\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1324\u001b[0;31m     \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1325\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1585\u001b[0m           or call_context_key not in self._function_cache.missed):\n\u001b[1;32m   1586\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1587\u001b[0;31m         \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1588\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1589\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   1518\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1519\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1520\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   1521\u001b[0m         self._function_attributes)\n\u001b[1;32m   1522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    705\u001b[0m                                           converted_func)\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, IndexedSlices,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    697\u001b[0m                   \u001b[0moptional_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautograph_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m                   \u001b[0mforce_conversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m               ), args, kwargs)\n\u001b[0m\u001b[1;32m    700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0;31m# Wrapping around a decorator allows checks like tf_inspect.getargspec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, owner, options, args, kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverted_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0meffective_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverted_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0meffective_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/mn/0x4pxw0n61lf479ndp07r0gr0000gn/T/tmp03oq9q1o.py\u001b[0m in \u001b[0;36mtf__logp_wrapper_xla\u001b[0;34m(array)\u001b[0m\n\u001b[1;32m      6\u001b[0m       \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m       \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m       \u001b[0mlogp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'compile'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxla\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConversionOptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecursive\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_conversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptional_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minternal_convert_user_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlogp_wrapper\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'inputs'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m       \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m       \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, owner, options, args, kwargs)\u001b[0m\n\u001b[1;32m    225\u001b[0m       \u001b[0mowner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSuperWrapperForDynamicAttrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0minspect_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misbuiltin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow.compiler.xla' has no attribute 'compile'"
     ]
    }
   ],
   "source": [
    "# Currently broken, not sure why\n",
    "array = tf.ones(40)\n",
    "\n",
    "@tf.function\n",
    "def logp_wrapper(array):\n",
    "    mu = array[:10]\n",
    "    sd = array[10:20]\n",
    "    y_0 = array[20:30]\n",
    "    y_1 = array[30:40]\n",
    "    logp = func(mu, sd, y_0, y_1)\n",
    "    grad = tf.gradients(logp, array)\n",
    "    return logp, grad\n",
    "\n",
    "@tf.function\n",
    "def logp_wrapper_xla(array):\n",
    "    logp, grad = xla.compile(logp_wrapper, inputs=[array])\n",
    "    return logp, grad\n",
    "\n",
    "logp_wrapper_xla(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350 µs ± 49.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit logp_wrapper(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc3 as pm3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm3.Model() as model:\n",
    "    mu = pm3.Normal('mu', 0, 1, shape=10)\n",
    "    sd = pm3.HalfNormal('sd', sd=1, transform=None, shape=10)\n",
    "    pm3.Normal('y_0', 0, 2 * sd, shape=10)\n",
    "    pm3.Normal('y_1', mu, 2 * sd, shape=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_pm3 = model.logp_dlogp_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.ones(func_pm3.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.1 µs ± 2.55 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "func_pm3.set_extra_values({})\n",
    "%timeit func_pm3(x0)"
   ]
  }
 ],
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